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Nathan Phelps

Nathan Phelps is a PhD student in Statistics at Western University, where he is a member of both the Wildland Fire Science Lab and the PHI Lab. Nathan has a BSc in Actuarial Science and Data Science and a MSc in Computer Science. After completing his MSc, he worked as a Research Associate in the Wildfire Analytics Lab at the University of Alberta, then joined the Financial Wellness Lab as a data engineer. He has transitioned to a part-time role in the lab during his PhD studies.

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Areas of Interest

Applying statistical and machine learning techniques to problems in personal finance, wildland fire science, and health care.


Phelps, N. & Metzler, A. (2023). Enhancing an existing algorithm for small-cardinality constrained portfolio optimisation. Journal of the Operational Research Society, 1-15.


Phelps, N. & Beverly, J. L. (2022). Classification of forest fuels in selected fire-prone ecosystems of Alberta, Canada—implications for crown fire behaviour prediction and fuel management. Annals of Forest Science, 79(1), 40.


Phelps, N., Cameron, H., Forbes, A. M., Schiks, T., Schroeder, D., & Beverly, J. L. (2022). The Alberta Wildland Fuels Inventory Program (AWFIP): data description and reference tables. Annals of Forest Science, 79(1), 28.


Phelps, N. & Woolford, D. G. (2021). Comparing calibrated statistical and machine learning methods for wildland fire occurrence prediction: A case study of human-caused fires in Lac La Biche, Alberta, Canada. International Journal of Wildland Fire, 30(11), 850-870.


Arntfield, R., VanBerlo, B., Alaifan, T., Phelps, N., White, M., Chaudhary, R., Ho, J., & Wu, D. (2021). Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study. BMJ Open, 11(3), e045120.


Phelps, N. & Woolford, D. G. (2021). Guidelines for effective evaluation and comparison of wildland fire occurrence prediction models. International Journal of Wildland Fire, 30(4), 225-240.


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